Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations418
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory186.0 KiB
Average record size in memory455.6 B

Variable types

Numeric6
Categorical5
Text3

Alerts

Family Size is highly overall correlated with Fare and 2 other fieldsHigh correlation
Fare is highly overall correlated with Family SizeHigh correlation
Parch is highly overall correlated with Family SizeHigh correlation
Sex is highly overall correlated with Survived and 1 other fieldsHigh correlation
SibSp is highly overall correlated with Family SizeHigh correlation
Survived is highly overall correlated with Sex and 1 other fieldsHigh correlation
Title is highly overall correlated with Sex and 1 other fieldsHigh correlation
PassengerId is uniformly distributedUniform
PassengerId has unique valuesUnique
Name has unique valuesUnique
SibSp has 283 (67.7%) zerosZeros
Parch has 324 (77.5%) zerosZeros

Reproduction

Analysis started2025-11-13 21:56:02.493823
Analysis finished2025-11-13 21:56:07.223291
Duration4.73 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

PassengerId
Real number (ℝ)

Uniform  Unique 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1100.5
Minimum892
Maximum1309
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-14T00:56:07.311358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum892
5-th percentile912.85
Q1996.25
median1100.5
Q31204.75
95-th percentile1288.15
Maximum1309
Range417
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation120.81046
Coefficient of variation (CV)0.10977779
Kurtosis-1.2
Mean1100.5
Median Absolute Deviation (MAD)104.5
Skewness0
Sum460009
Variance14595.167
MonotonicityStrictly increasing
2025-11-14T00:56:07.419767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13091
 
0.2%
8921
 
0.2%
12931
 
0.2%
12921
 
0.2%
12911
 
0.2%
12901
 
0.2%
12891
 
0.2%
12881
 
0.2%
12871
 
0.2%
12861
 
0.2%
Other values (408)408
97.6%
ValueCountFrequency (%)
8921
0.2%
8931
0.2%
8941
0.2%
8951
0.2%
8961
0.2%
8971
0.2%
8981
0.2%
8991
0.2%
9001
0.2%
9011
0.2%
ValueCountFrequency (%)
13091
0.2%
13081
0.2%
13071
0.2%
13061
0.2%
13051
0.2%
13041
0.2%
13031
0.2%
13021
0.2%
13011
0.2%
13001
0.2%

Survived
Categorical

High correlation 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
0
266 
1
152 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0266
63.6%
1152
36.4%

Length

2025-11-14T00:56:07.510588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T00:56:07.595679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0266
63.6%
1152
36.4%

Most occurring characters

ValueCountFrequency (%)
0266
63.6%
1152
36.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0266
63.6%
1152
36.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0266
63.6%
1152
36.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0266
63.6%
1152
36.4%

Pclass
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
3
218 
1
107 
2
93 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3218
52.2%
1107
25.6%
293
22.2%

Length

2025-11-14T00:56:07.657086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T00:56:07.710613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3218
52.2%
1107
25.6%
293
22.2%

Most occurring characters

ValueCountFrequency (%)
3218
52.2%
1107
25.6%
293
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3218
52.2%
1107
25.6%
293
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3218
52.2%
1107
25.6%
293
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3218
52.2%
1107
25.6%
293
22.2%

Name
Text

Unique 

Distinct418
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size34.6 KiB
2025-11-14T00:56:08.634468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length63
Median length51
Mean length27.483254
Min length13

Characters and Unicode

Total characters11488
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique418 ?
Unique (%)100.0%

Sample

1st rowKelly, Mr. James
2nd rowWilkes, Mrs. James (Ellen Needs)
3rd rowMyles, Mr. Thomas Francis
4th rowWirz, Mr. Albert
5th rowHirvonen, Mrs. Alexander (Helga E Lindqvist)
ValueCountFrequency (%)
mr242
 
14.0%
miss78
 
4.5%
mrs72
 
4.2%
john28
 
1.6%
william23
 
1.3%
master21
 
1.2%
charles16
 
0.9%
joseph15
 
0.9%
thomas14
 
0.8%
james14
 
0.8%
Other values (825)1202
69.7%
2025-11-14T00:56:08.980166image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1309
 
11.4%
r971
 
8.5%
e822
 
7.2%
a786
 
6.8%
s628
 
5.5%
i621
 
5.4%
n596
 
5.2%
l526
 
4.6%
M515
 
4.5%
o467
 
4.1%
Other values (48)4247
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)11488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1309
 
11.4%
r971
 
8.5%
e822
 
7.2%
a786
 
6.8%
s628
 
5.5%
i621
 
5.4%
n596
 
5.2%
l526
 
4.6%
M515
 
4.5%
o467
 
4.1%
Other values (48)4247
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)11488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1309
 
11.4%
r971
 
8.5%
e822
 
7.2%
a786
 
6.8%
s628
 
5.5%
i621
 
5.4%
n596
 
5.2%
l526
 
4.6%
M515
 
4.5%
o467
 
4.1%
Other values (48)4247
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)11488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1309
 
11.4%
r971
 
8.5%
e822
 
7.2%
a786
 
6.8%
s628
 
5.5%
i621
 
5.4%
n596
 
5.2%
l526
 
4.6%
M515
 
4.5%
o467
 
4.1%
Other values (48)4247
37.0%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size25.3 KiB
male
266 
female
152 

Length

Max length6
Median length4
Mean length4.7272727
Min length4

Characters and Unicode

Total characters1976
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowmale
4th rowmale
5th rowfemale

Common Values

ValueCountFrequency (%)
male266
63.6%
female152
36.4%

Length

2025-11-14T00:56:09.082366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T00:56:09.140069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
male266
63.6%
female152
36.4%

Most occurring characters

ValueCountFrequency (%)
e570
28.8%
m418
21.2%
a418
21.2%
l418
21.2%
f152
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)1976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e570
28.8%
m418
21.2%
a418
21.2%
l418
21.2%
f152
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e570
28.8%
m418
21.2%
a418
21.2%
l418
21.2%
f152
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e570
28.8%
m418
21.2%
a418
21.2%
l418
21.2%
f152
 
7.7%

Age
Real number (ℝ)

Distinct79
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.599282
Minimum0.17
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-14T00:56:09.217256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.17
5-th percentile10
Q123
median27
Q335.75
95-th percentile55
Maximum76
Range75.83
Interquartile range (IQR)12.75

Descriptive statistics

Standard deviation12.70377
Coefficient of variation (CV)0.42919182
Kurtosis0.92398799
Mean29.599282
Median Absolute Deviation (MAD)5
Skewness0.66074704
Sum12372.5
Variance161.38577
MonotonicityNot monotonic
2025-11-14T00:56:09.328488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2798
23.4%
2417
 
4.1%
2117
 
4.1%
2216
 
3.8%
3015
 
3.6%
1813
 
3.1%
2612
 
2.9%
2311
 
2.6%
2511
 
2.6%
2910
 
2.4%
Other values (69)198
47.4%
ValueCountFrequency (%)
0.171
 
0.2%
0.331
 
0.2%
0.751
 
0.2%
0.831
 
0.2%
0.921
 
0.2%
13
0.7%
22
0.5%
31
 
0.2%
51
 
0.2%
63
0.7%
ValueCountFrequency (%)
761
 
0.2%
671
 
0.2%
643
0.7%
632
0.5%
621
 
0.2%
612
0.5%
60.51
 
0.2%
603
0.7%
591
 
0.2%
581
 
0.2%

SibSp
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44736842
Minimum0
Maximum8
Zeros283
Zeros (%)67.7%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-14T00:56:09.415555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89675956
Coefficient of variation (CV)2.0045214
Kurtosis26.498712
Mean0.44736842
Median Absolute Deviation (MAD)0
Skewness4.1683366
Sum187
Variance0.80417771
MonotonicityNot monotonic
2025-11-14T00:56:09.586263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0283
67.7%
1110
 
26.3%
214
 
3.3%
34
 
1.0%
44
 
1.0%
82
 
0.5%
51
 
0.2%
ValueCountFrequency (%)
0283
67.7%
1110
 
26.3%
214
 
3.3%
34
 
1.0%
44
 
1.0%
51
 
0.2%
82
 
0.5%
ValueCountFrequency (%)
82
 
0.5%
51
 
0.2%
44
 
1.0%
34
 
1.0%
214
 
3.3%
1110
 
26.3%
0283
67.7%

Parch
Real number (ℝ)

High correlation  Zeros 

Distinct8
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3923445
Minimum0
Maximum9
Zeros324
Zeros (%)77.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-14T00:56:09.652822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.98142888
Coefficient of variation (CV)2.5014468
Kurtosis31.412513
Mean0.3923445
Median Absolute Deviation (MAD)0
Skewness4.6544617
Sum164
Variance0.96320264
MonotonicityNot monotonic
2025-11-14T00:56:09.721628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0324
77.5%
152
 
12.4%
233
 
7.9%
33
 
0.7%
42
 
0.5%
92
 
0.5%
61
 
0.2%
51
 
0.2%
ValueCountFrequency (%)
0324
77.5%
152
 
12.4%
233
 
7.9%
33
 
0.7%
42
 
0.5%
51
 
0.2%
61
 
0.2%
92
 
0.5%
ValueCountFrequency (%)
92
 
0.5%
61
 
0.2%
51
 
0.2%
42
 
0.5%
33
 
0.7%
233
 
7.9%
152
 
12.4%
0324
77.5%

Ticket
Text

Distinct363
Distinct (%)86.8%
Missing0
Missing (%)0.0%
Memory size26.2 KiB
2025-11-14T00:56:09.931618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length18
Median length17
Mean length6.8755981
Min length3

Characters and Unicode

Total characters2874
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique321 ?
Unique (%)76.8%

Sample

1st row330911
2nd row363272
3rd row240276
4th row315154
5th row3101298
ValueCountFrequency (%)
pc32
 
5.9%
c.a19
 
3.5%
ca8
 
1.5%
soton/o.q8
 
1.5%
sc/paris7
 
1.3%
176085
 
0.9%
a/55
 
0.9%
w./c5
 
0.9%
25
 
0.9%
23434
 
0.7%
Other values (383)445
82.0%
2025-11-14T00:56:10.264294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3364
12.7%
1311
10.8%
2268
9.3%
7207
 
7.2%
6206
 
7.2%
0204
 
7.1%
5195
 
6.8%
4188
 
6.5%
8144
 
5.0%
9137
 
4.8%
Other values (22)650
22.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)2874
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3364
12.7%
1311
10.8%
2268
9.3%
7207
 
7.2%
6206
 
7.2%
0204
 
7.1%
5195
 
6.8%
4188
 
6.5%
8144
 
5.0%
9137
 
4.8%
Other values (22)650
22.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2874
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3364
12.7%
1311
10.8%
2268
9.3%
7207
 
7.2%
6206
 
7.2%
0204
 
7.1%
5195
 
6.8%
4188
 
6.5%
8144
 
5.0%
9137
 
4.8%
Other values (22)650
22.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2874
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3364
12.7%
1311
10.8%
2268
9.3%
7207
 
7.2%
6206
 
7.2%
0204
 
7.1%
5195
 
6.8%
4188
 
6.5%
8144
 
5.0%
9137
 
4.8%
Other values (22)650
22.6%

Fare
Real number (ℝ)

High correlation 

Distinct169
Distinct (%)40.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean35.627188
Minimum0
Maximum512.3292
Zeros2
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-14T00:56:10.376485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.2292
Q17.8958
median14.4542
Q331.5
95-th percentile151.55
Maximum512.3292
Range512.3292
Interquartile range (IQR)23.6042

Descriptive statistics

Standard deviation55.907576
Coefficient of variation (CV)1.5692391
Kurtosis17.921595
Mean35.627188
Median Absolute Deviation (MAD)6.825
Skewness3.6872133
Sum14856.538
Variance3125.6571
MonotonicityNot monotonic
2025-11-14T00:56:10.489241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.7521
 
5.0%
2619
 
4.5%
1317
 
4.1%
8.0517
 
4.1%
7.895811
 
2.6%
10.511
 
2.6%
7.77510
 
2.4%
7.2259
 
2.2%
7.22929
 
2.2%
8.66258
 
1.9%
Other values (159)285
68.2%
ValueCountFrequency (%)
02
 
0.5%
3.17081
 
0.2%
6.43752
 
0.5%
6.49581
 
0.2%
6.951
 
0.2%
72
 
0.5%
7.052
 
0.5%
7.2259
2.2%
7.22929
2.2%
7.255
1.2%
ValueCountFrequency (%)
512.32921
 
0.2%
2632
 
0.5%
262.3755
1.2%
247.52081
 
0.2%
227.5251
 
0.2%
221.77923
0.7%
211.54
1.0%
211.33751
 
0.2%
164.86672
 
0.5%
151.552
 
0.5%

Cabin
Text

Distinct77
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Memory size26.0 KiB
2025-11-14T00:56:10.655433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length7
Mean length6.3636364
Min length1

Characters and Unicode

Total characters2660
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)14.8%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowUnknown
5th rowUnknown
ValueCountFrequency (%)
unknown327
73.5%
f4
 
0.9%
b573
 
0.7%
b633
 
0.7%
b663
 
0.7%
b593
 
0.7%
b452
 
0.4%
c252
 
0.4%
c272
 
0.4%
c782
 
0.4%
Other values (81)94
 
21.1%
2025-11-14T00:56:10.906227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n981
36.9%
U327
 
12.3%
k327
 
12.3%
o327
 
12.3%
w327
 
12.3%
C43
 
1.6%
534
 
1.3%
133
 
1.2%
B32
 
1.2%
630
 
1.1%
Other values (13)199
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)2660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n981
36.9%
U327
 
12.3%
k327
 
12.3%
o327
 
12.3%
w327
 
12.3%
C43
 
1.6%
534
 
1.3%
133
 
1.2%
B32
 
1.2%
630
 
1.1%
Other values (13)199
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n981
36.9%
U327
 
12.3%
k327
 
12.3%
o327
 
12.3%
w327
 
12.3%
C43
 
1.6%
534
 
1.3%
133
 
1.2%
B32
 
1.2%
630
 
1.1%
Other values (13)199
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n981
36.9%
U327
 
12.3%
k327
 
12.3%
o327
 
12.3%
w327
 
12.3%
C43
 
1.6%
534
 
1.3%
133
 
1.2%
B32
 
1.2%
630
 
1.1%
Other values (13)199
 
7.5%

Embarked
Categorical

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size23.8 KiB
S
270 
C
102 
Q
46 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters418
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowQ
2nd rowS
3rd rowQ
4th rowS
5th rowS

Common Values

ValueCountFrequency (%)
S270
64.6%
C102
 
24.4%
Q46
 
11.0%

Length

2025-11-14T00:56:10.994777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T00:56:11.048564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
s270
64.6%
c102
 
24.4%
q46
 
11.0%

Most occurring characters

ValueCountFrequency (%)
S270
64.6%
C102
 
24.4%
Q46
 
11.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)418
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S270
64.6%
C102
 
24.4%
Q46
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)418
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S270
64.6%
C102
 
24.4%
Q46
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)418
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S270
64.6%
C102
 
24.4%
Q46
 
11.0%

Title
Categorical

High correlation 

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size24.5 KiB
Mr
240 
Miss
79 
Mrs
72 
Master
 
21
Officer
 
5

Length

Max length7
Median length2
Mean length2.8229665
Min length2

Characters and Unicode

Total characters1180
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowMr
2nd rowMrs
3rd rowMr
4th rowMr
5th rowMrs

Common Values

ValueCountFrequency (%)
Mr240
57.4%
Miss79
 
18.9%
Mrs72
 
17.2%
Master21
 
5.0%
Officer5
 
1.2%
Royalty1
 
0.2%

Length

2025-11-14T00:56:11.123934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-14T00:56:11.190747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mr240
57.4%
miss79
 
18.9%
mrs72
 
17.2%
master21
 
5.0%
officer5
 
1.2%
royalty1
 
0.2%

Most occurring characters

ValueCountFrequency (%)
M412
34.9%
r338
28.6%
s251
21.3%
i84
 
7.1%
e26
 
2.2%
a22
 
1.9%
t22
 
1.9%
f10
 
0.8%
O5
 
0.4%
c5
 
0.4%
Other values (4)5
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)1180
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M412
34.9%
r338
28.6%
s251
21.3%
i84
 
7.1%
e26
 
2.2%
a22
 
1.9%
t22
 
1.9%
f10
 
0.8%
O5
 
0.4%
c5
 
0.4%
Other values (4)5
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1180
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M412
34.9%
r338
28.6%
s251
21.3%
i84
 
7.1%
e26
 
2.2%
a22
 
1.9%
t22
 
1.9%
f10
 
0.8%
O5
 
0.4%
c5
 
0.4%
Other values (4)5
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1180
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M412
34.9%
r338
28.6%
s251
21.3%
i84
 
7.1%
e26
 
2.2%
a22
 
1.9%
t22
 
1.9%
f10
 
0.8%
O5
 
0.4%
c5
 
0.4%
Other values (4)5
 
0.4%

Family Size
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8397129
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2025-11-14T00:56:11.263318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile4
Maximum11
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.519072
Coefficient of variation (CV)0.82571144
Kurtosis13.431226
Mean1.8397129
Median Absolute Deviation (MAD)0
Skewness3.1685425
Sum769
Variance2.3075798
MonotonicityNot monotonic
2025-11-14T00:56:11.333614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1253
60.5%
274
 
17.7%
357
 
13.6%
414
 
3.3%
57
 
1.7%
114
 
1.0%
74
 
1.0%
63
 
0.7%
82
 
0.5%
ValueCountFrequency (%)
1253
60.5%
274
 
17.7%
357
 
13.6%
414
 
3.3%
57
 
1.7%
63
 
0.7%
74
 
1.0%
82
 
0.5%
114
 
1.0%
ValueCountFrequency (%)
114
 
1.0%
82
 
0.5%
74
 
1.0%
63
 
0.7%
57
 
1.7%
414
 
3.3%
357
 
13.6%
274
 
17.7%
1253
60.5%

Interactions

2025-11-14T00:56:06.540528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:04.194661image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:04.651855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.121875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.596733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.102154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.609994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:04.280835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:04.729701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.198399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.665044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.174494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.686117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:04.359480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:04.808739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.283332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.739450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.250346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.760916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:04.437270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:04.891961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.364325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.814875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.329299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.829552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:04.509383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:04.968791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.441320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.881566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.398466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.900069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:04.581076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.044812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:05.518964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.039670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-14T00:56:06.470457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-14T00:56:11.402317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeEmbarkedFamily SizeFareParchPassengerIdPclassSexSibSpSurvivedTitle
Age1.0000.139-0.0290.278-0.103-0.0140.3500.062-0.0050.0620.335
Embarked0.1391.0000.1020.2400.1130.0600.3080.1090.1010.1090.212
Family Size-0.0290.1021.0000.5030.7840.0090.0910.1690.8460.1690.197
Fare0.2780.2400.5031.0000.3780.0200.4750.1540.4410.1540.168
Parch-0.1030.1130.7840.3781.0000.0510.0000.2130.4120.2130.227
PassengerId-0.0140.0600.0090.0200.0511.0000.0540.000-0.0100.0000.055
Pclass0.3500.3080.0910.4750.0000.0541.0000.1060.1130.1060.196
Sex0.0620.1090.1690.1540.2130.0000.1061.0000.1360.9950.995
SibSp-0.0050.1010.8460.4410.412-0.0100.1130.1361.0000.1360.195
Survived0.0620.1090.1690.1540.2130.0000.1060.9950.1361.0000.995
Title0.3350.2120.1970.1680.2270.0550.1960.9950.1950.9951.000

Missing values

2025-11-14T00:56:07.021653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-14T00:56:07.142647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedTitleFamily Size
089203Kelly, Mr. Jamesmale34.5003309117.8292UnknownQMr1
189313Wilkes, Mrs. James (Ellen Needs)female47.0103632727.0000UnknownSMrs2
289402Myles, Mr. Thomas Francismale62.0002402769.6875UnknownQMr1
389503Wirz, Mr. Albertmale27.0003151548.6625UnknownSMr1
489613Hirvonen, Mrs. Alexander (Helga E Lindqvist)female22.011310129812.2875UnknownSMrs3
589703Svensson, Mr. Johan Cervinmale14.00075389.2250UnknownSMr1
689813Connolly, Miss. Katefemale30.0003309727.6292UnknownQMiss1
789902Caldwell, Mr. Albert Francismale26.01124873829.0000UnknownSMr3
890013Abrahim, Mrs. Joseph (Sophie Halaut Easu)female18.00026577.2292UnknownCMrs1
990103Davies, Mr. John Samuelmale21.020A/4 4887124.1500UnknownSMr3
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedTitleFamily Size
408130013Riordan, Miss. Johanna Hannah""female27.0003349157.7208UnknownQMiss1
409130113Peacock, Miss. Treasteallfemale3.011SOTON/O.Q. 310131513.7750UnknownSMiss3
410130213Naughton, Miss. Hannahfemale27.0003652377.7500UnknownQMiss1
411130311Minahan, Mrs. William Edward (Lillian E Thorpe)female37.0101992890.0000C78QMrs2
412130413Henriksson, Miss. Jenny Lovisafemale28.0003470867.7750UnknownSMiss1
413130503Spector, Mr. Woolfmale27.000A.5. 32368.0500UnknownSMr1
414130611Oliva y Ocana, Dona. Ferminafemale39.000PC 17758108.9000C105CRoyalty1
415130703Saether, Mr. Simon Sivertsenmale38.500SOTON/O.Q. 31012627.2500UnknownSMr1
416130803Ware, Mr. Frederickmale27.0003593098.0500UnknownSMr1
417130903Peter, Master. Michael Jmale27.011266822.3583UnknownCMaster3